Given the two kinds of GNN pre-training tasks, it is essential to combine node- and graph-level optimization goals [1]}, since they capture the graph characteristics in different views.
Existing approaches
either adopt a two-stage approach arranging multi-level pre-training tasks sequentially [1]},
or frame them in a multi-task learning manner [3]}.
In this way, each individual pre-training task is not aware of all the optimization goals at different levels, which might result in locally optimal representations w.r.t. some specific level (e.g., node- or graph-level).
Ideally, a good pre-training task can capture node- and graph-level characteristics simultaneously in order to derive more comprehensive node (and graph) representations.
